Overview

Dataset statistics

Number of variables10
Number of observations5695
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory489.4 KiB
Average record size in memory88.0 B

Variable types

Numeric9
Unsupported1

Alerts

monetary is highly overall correlated with qtd_compras and 1 other fieldsHigh correlation
frequency is highly overall correlated with qtd_comprasHigh correlation
qtd_compras is highly overall correlated with monetary and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_comprasHigh correlation
prod_variety is highly overall correlated with monetaryHigh correlation
qtd_returns is highly overall correlated with qtd_comprasHigh correlation
monetary is highly skewed (γ1 = 22.58539738)Skewed
avg_unit_price is highly skewed (γ1 = 40.02045293)Skewed
avg_qtd_items is highly skewed (γ1 = 74.21926571)Skewed
qtd_returns is highly skewed (γ1 = 71.05769911)Skewed
customer_id has unique valuesUnique
relationship_duration is an unsupported type, check if it needs cleaning or further analysisUnsupported
qtd_returns has 4191 (73.6%) zerosZeros

Reproduction

Analysis started2023-06-24 11:07:51.237774
Analysis finished2023-06-24 11:08:02.793753
Duration11.56 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.599
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:02.893045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12699.1
Q114288.5
median16227
Q318210.5
95-th percentile21731.1
Maximum22709
Range10363
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.2419
Coefficient of variation (CV)0.16916509
Kurtosis-0.82111592
Mean16600.599
Median Absolute Deviation (MAD)1963
Skewness0.44127055
Sum94540411
Variance7886222.5
MonotonicityStrictly increasing
2023-06-24T08:08:03.048918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12346 1
 
< 0.1%
17596 1
 
< 0.1%
17562 1
 
< 0.1%
17561 1
 
< 0.1%
17560 1
 
< 0.1%
17557 1
 
< 0.1%
17556 1
 
< 0.1%
17555 1
 
< 0.1%
17554 1
 
< 0.1%
17553 1
 
< 0.1%
Other values (5685) 5685
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
22709 1
< 0.1%
22708 1
< 0.1%
22707 1
< 0.1%
22706 1
< 0.1%
22705 1
< 0.1%
22704 1
< 0.1%
22700 1
< 0.1%
22699 1
< 0.1%
22696 1
< 0.1%
22695 1
< 0.1%

monetary
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5449
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1774.3035
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:03.193021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.171
Q1236.135
median613.2
Q31570.74
95-th percentile5309.696
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.605

Descriptive statistics

Standard deviation7582.2097
Coefficient of variation (CV)4.2733443
Kurtosis675.61569
Mean1774.3035
Median Absolute Deviation (MAD)479.19
Skewness22.585397
Sum10104658
Variance57489903
MonotonicityNot monotonic
2023-06-24T08:08:03.331008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
1.25 8
 
0.1%
2.95 8
 
0.1%
4.95 8
 
0.1%
3.75 7
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
7.5 6
 
0.1%
5.95 6
 
0.1%
4.25 6
 
0.1%
Other values (5439) 5623
98.7%
ValueCountFrequency (%)
0.42 1
 
< 0.1%
0.65 1
 
< 0.1%
0.79 1
 
< 0.1%
0.84 4
0.1%
0.85 3
 
0.1%
1.07 1
 
< 0.1%
1.25 8
0.1%
1.44 1
 
< 0.1%
1.65 7
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

frequency
Real number (ℝ)

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54729666
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:03.484394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.01104363
Q10.024926433
median1
Q31
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.97507357

Descriptive statistics

Standard deviation0.55025739
Coefficient of variation (CV)1.0054097
Kurtosis139.13695
Mean0.54729666
Median Absolute Deviation (MAD)0
Skewness4.8586449
Sum3116.8545
Variance0.3027832
MonotonicityNot monotonic
2023-06-24T08:08:03.637464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2879
50.6%
2 47
 
0.8%
0.0625 18
 
0.3%
0.02777777778 17
 
0.3%
0.02380952381 16
 
0.3%
0.09090909091 15
 
0.3%
0.08333333333 15
 
0.3%
0.02941176471 14
 
0.2%
0.03448275862 14
 
0.2%
0.02127659574 13
 
0.2%
Other values (1216) 2647
46.5%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
< 0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
< 0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.1%
2 47
 
0.8%
1.142857143 1
 
< 0.1%
1 2879
50.6%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%

qtd_compras
Real number (ℝ)

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4712906
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:03.803409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8132943
Coefficient of variation (CV)1.9627554
Kurtosis302.09072
Mean3.4712906
Median Absolute Deviation (MAD)0
Skewness13.192783
Sum19769
Variance46.420979
MonotonicityNot monotonic
2023-06-24T08:08:03.950420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2871
50.4%
2 825
 
14.5%
3 503
 
8.8%
4 394
 
6.9%
5 237
 
4.2%
6 173
 
3.0%
7 138
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
10 55
 
1.0%
Other values (46) 332
 
5.8%
ValueCountFrequency (%)
1 2871
50.4%
2 825
 
14.5%
3 503
 
8.8%
4 394
 
6.9%
5 237
 
4.2%
6 173
 
3.0%
7 138
 
2.4%
8 98
 
1.7%
9 69
 
1.2%
10 55
 
1.0%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%
57 1
< 0.1%

recency
Real number (ℝ)

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.92766
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:04.098624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.64636
Coefficient of variation (CV)0.95483282
Kurtosis-0.64262595
Mean116.92766
Median Absolute Deviation (MAD)61
Skewness0.81431448
Sum665903
Variance12464.91
MonotonicityNot monotonic
2023-06-24T08:08:04.250600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 92
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
17 79
 
1.4%
9 79
 
1.4%
7 78
 
1.4%
15 66
 
1.2%
Other values (294) 4820
84.6%
ValueCountFrequency (%)
0 37
 
0.6%
1 110
1.9%
2 92
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 23
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

avg_unit_price
Real number (ℝ)

Distinct5266
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7157081
Minimum0.06
Maximum434.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:04.414603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.2510829
Q12.2175
median3.0477976
Q34.25
95-th percentile6.6995626
Maximum434.65
Range434.59
Interquartile range (IQR)2.0325

Descriptive statistics

Standard deviation7.8490615
Coefficient of variation (CV)2.1123999
Kurtosis1952.83
Mean3.7157081
Median Absolute Deviation (MAD)0.95835623
Skewness40.020453
Sum21160.958
Variance61.607767
MonotonicityNot monotonic
2023-06-24T08:08:04.560539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 26
 
0.5%
4.95 20
 
0.4%
0.85 18
 
0.3%
3.75 16
 
0.3%
2.95 15
 
0.3%
1.65 14
 
0.2%
2.08 13
 
0.2%
0.42 13
 
0.2%
12.75 12
 
0.2%
7.95 12
 
0.2%
Other values (5256) 5536
97.2%
ValueCountFrequency (%)
0.06 1
< 0.1%
0.1225 1
< 0.1%
0.17 2
< 0.1%
0.2327777778 1
< 0.1%
0.29 2
< 0.1%
0.32 1
< 0.1%
0.33 1
< 0.1%
0.355 2
< 0.1%
0.358 1
< 0.1%
0.3666666667 1
< 0.1%
ValueCountFrequency (%)
434.65 1
< 0.1%
295 1
< 0.1%
125 1
< 0.1%
110 2
< 0.1%
74.975 1
< 0.1%
66.475 1
< 0.1%
59.73333333 1
< 0.1%
54.3 1
< 0.1%
51.71 1
< 0.1%
39.95 1
< 0.1%

avg_qtd_items
Real number (ℝ)

Distinct4270
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.910403
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:04.709659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2301099
Q12.9541958
median8.1594533
Q312.903191
95-th percentile39.548387
Maximum74215
Range74214
Interquartile range (IQR)9.9489957

Descriptive statistics

Standard deviation988.80893
Coefficient of variation (CV)31.989519
Kurtosis5566.838
Mean30.910403
Median Absolute Deviation (MAD)5.0772615
Skewness74.219266
Sum176034.74
Variance977743.1
MonotonicityNot monotonic
2023-06-24T08:08:04.857174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 237
 
4.2%
2 51
 
0.9%
4 33
 
0.6%
10 31
 
0.5%
1.5 29
 
0.5%
12 27
 
0.5%
3 25
 
0.4%
6 23
 
0.4%
1.333333333 20
 
0.4%
5 19
 
0.3%
Other values (4260) 5200
91.3%
ValueCountFrequency (%)
1 237
4.2%
1.052631579 1
 
< 0.1%
1.055555556 1
 
< 0.1%
1.057142857 1
 
< 0.1%
1.0625 2
 
< 0.1%
1.066666667 1
 
< 0.1%
1.071428571 1
 
< 0.1%
1.08 1
 
< 0.1%
1.094339623 1
 
< 0.1%
1.1 5
 
0.1%
ValueCountFrequency (%)
74215 1
< 0.1%
4300 1
< 0.1%
3906 1
< 0.1%
2140 1
< 0.1%
2000 1
< 0.1%
1802.8 1
< 0.1%
1756.5 1
< 0.1%
1440 1
< 0.1%
1404 1
< 0.1%
1350 1
< 0.1%

prod_variety
Real number (ℝ)

Distinct439
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.669535
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:05.017677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384.5
95-th percentile241.3
Maximum1786
Range1785
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation101.73049
Coefficient of variation (CV)1.4601861
Kurtosis43.880529
Mean69.669535
Median Absolute Deviation (MAD)28
Skewness4.7034268
Sum396768
Variance10349.092
MonotonicityNot monotonic
2023-06-24T08:08:05.168002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 279
 
4.9%
2 149
 
2.6%
3 112
 
2.0%
10 101
 
1.8%
5 98
 
1.7%
9 96
 
1.7%
6 93
 
1.6%
8 93
 
1.6%
11 92
 
1.6%
4 90
 
1.6%
Other values (429) 4492
78.9%
ValueCountFrequency (%)
1 279
4.9%
2 149
2.6%
3 112
2.0%
4 90
 
1.6%
5 98
 
1.7%
6 93
 
1.6%
7 90
 
1.6%
8 93
 
1.6%
9 96
 
1.7%
10 101
 
1.8%
ValueCountFrequency (%)
1786 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
1109 1
< 0.1%
884 1
< 0.1%
817 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%

relationship_duration
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size89.0 KiB

qtd_returns
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct214
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.268306
Minimum0
Maximum74215
Zeros4191
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size89.0 KiB
2023-06-24T08:08:05.323432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38.3
Maximum74215
Range74215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1004.3217
Coefficient of variation (CV)32.11948
Kurtosis5232.3595
Mean31.268306
Median Absolute Deviation (MAD)0
Skewness71.057699
Sum178073
Variance1008662.1
MonotonicityNot monotonic
2023-06-24T08:08:05.468290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4191
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
6 78
 
1.4%
5 61
 
1.1%
12 52
 
0.9%
7 44
 
0.8%
8 43
 
0.8%
Other values (204) 713
 
12.5%
ValueCountFrequency (%)
0 4191
73.6%
1 169
 
3.0%
2 150
 
2.6%
3 105
 
1.8%
4 89
 
1.6%
5 61
 
1.1%
6 78
 
1.4%
7 44
 
0.8%
8 43
 
0.8%
9 41
 
0.7%
ValueCountFrequency (%)
74215 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

Interactions

2023-06-24T08:08:01.195475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:51.443337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.754447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.864938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.040936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.134772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.311017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.463928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.991061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.326826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:51.581167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.876098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.994137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.159789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.258015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.435536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.606359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.132269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.454038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:51.699560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.990855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.119263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.287298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.381218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.563627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.742280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.259393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.592544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:51.829176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.118654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.253599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.413304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.512519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.694579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.906315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.399029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.734386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:51.941894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.233459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.377599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.523577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.630656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.813048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.046714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.525053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.872574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.069300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.359869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.514709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.648625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.758588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.941896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.197426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.660018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.995034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.185266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.474694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.637839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.760435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.876762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.059346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.335510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.781657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:02.138175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.316951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.601278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.773017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:55.887378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.027235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.192784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.682744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:00.921646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:02.275369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:52.446365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:53.731097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:54.904590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:56.009566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:57.171888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:58.329775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:07:59.841196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T08:08:01.057611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-24T08:08:05.597962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idmonetaryfrequencyqtd_comprasrecencyavg_unit_priceavg_qtd_itemsprod_varietyqtd_returns
customer_id1.000-0.1810.380-0.3830.2450.217-0.493-0.017-0.277
monetary-0.1811.000-0.4530.644-0.4260.0780.3220.7950.433
frequency0.380-0.4531.000-0.7990.4860.153-0.321-0.312-0.366
qtd_compras-0.3830.644-0.7991.000-0.597-0.1660.3610.4500.538
recency0.245-0.4260.486-0.5971.0000.203-0.272-0.327-0.320
avg_unit_price0.2170.0780.153-0.1660.2031.000-0.3670.064-0.062
avg_qtd_items-0.4930.322-0.3210.361-0.272-0.3671.000-0.1240.297
prod_variety-0.0170.795-0.3120.450-0.3270.064-0.1241.0000.270
qtd_returns-0.2770.433-0.3660.538-0.320-0.0620.2970.2701.000

Missing values

2023-06-24T08:08:02.479573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-24T08:08:02.690936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idmonetaryfrequencyqtd_comprasrecencyavg_unit_priceavg_qtd_itemsprod_varietyrelationship_durationqtd_returns
012346.077183.601.00000013251.04000074215.00000010 days74215.0
112347.04310.000.019126722.64401113.505495103365 days0.0
212348.01437.240.0140854750.69296386.37037021283 days0.0
312349.01457.551.0000001184.2375008.750000720 days0.0
412350.0294.401.00000013101.58125012.250000160 days0.0
512352.01385.740.0268207364.0754556.83116957260 days63.0
612353.089.001.00000012046.0750005.00000040 days0.0
712354.01079.401.00000012324.5037939.137931580 days0.0
812355.0459.401.00000012144.20384618.461538130 days0.0
912356.02487.430.0098683222.94603427.12069052303 days0.0
customer_idmonetaryfrequencyqtd_comprasrecencyavg_unit_priceavg_qtd_itemsprod_varietyrelationship_durationqtd_returns
568522695.06083.951.0114.2560742.7437046750 days0.0
568622696.07150.071.0114.2925942.8743327480 days0.0
568722699.03686.801.0115.7723153.4039412030 days0.0
568822700.04839.421.0117.25451617.322581550 days0.0
568922704.017.901.0111.2785712.00000070 days0.0
569022705.03.351.0111.6750001.00000020 days0.0
569122706.05699.001.0114.3209462.7555216340 days0.0
569222707.06756.061.0104.1759042.7534257300 days0.0
569322708.03217.201.0106.26966111.084746560 days0.0
569422709.03950.721.0106.3643783.3686642170 days0.0